Rejects in Production
By reducing the reject rate, a company’s economic performance can be significantly improved. We discuss the most common causes of waste in production and present measures to prevent rejects.
Introduction
What are rejects?
The term rejects refers to manufactured products that do not meet the minimum quality requirements. As a result, they cannot be sold or used and must either be disposed of or reworked. Terms with similar meaning are scrap, usually referring to leftover materials that need to be reworked or recycled, and waste, a broader term including defects as well as excess product.
Rejects are among the most costly losses for manufacturing companies, as these unusable products consume materials and other resources without generating any revenue.
Therefore, companies strive to measure and minimize rejects as much as possible.
The following sections provide an overview of the causes of rejects, methods for accurately capturing reject data, and measures to reduce reject rates.
In manufacturing, the reject rate is a commonly used metric to measure rejects.It indicates the proportion of defective products in relation to the total quantity produced.
Causes
Causes of rejects
To effectively reduce production rejects, they should be measured as precisely as possible and categorized based on predefined criteria and causes. A solid data foundation is essential for identifying and eliminating the specific sources of defects.
Example:
Tool wear can lead to rejects, just like low-quality materials. When data on material and tool usage is linked to reject data, companies can identify which tools or materials contribute to higher reject rates. As a result, they can replace unsuitable tools or switch to higher-quality materials.
Categorization of rejects based on causes
Possible causes of rejects vary depending on the type of production.
One of the most common reasons for rejects is quality fluctuations that occur during machine setup and startup. Rejects linked to machine setup / startup often result from unstable process conditions or target values that have not yet been reached. Optimizing setup and startup processes is therefore one of the most common starting points for reducing the reject rate.
Below are additional key sources of rejects along with relevant examples.
Manual input errors / User-related errors
- Manual input errors, incorrect machine settings
- Wrong tool selection
- Wrong material selection
- Communication errors
Suboptimal maintenance times and processes
- Delayed maintenance
- Undetected wear or defects
- Insufficiently trained maintenance staff
- Use of inappropriate spare parts
Material and tool quality
- Low-Quality materials (e.g. sheets with thickness tolerance deviations, paint with excessive viscosity)
- Incorrect labeling / Mix-Ups
- Hidden tool damage (e.g. microcracks, dull cutting edges)
Process instabilities
- Fluctuating process parameters (e.g. variations in temperature, pressure)
- Insufficient synchronization between consecutive process steps
- Process sensitivity to external influences (e.g. ambient temperature, humidity affecting process stability)
- Inadequate process control with changing batches or products
External conditions
- Fluctuating temperatures, temperature differences between production and storage areas
- High humidity
- Dust or contaminants
- Electrostatic charges
Design and planning errors
- Incomplete specifications for production
- Inadequate consideration of material properties during design
- Missing or unclear tolerance definitions in drawing or CAD models
- Insufficient coordination between design, manufacturing and quality assurance
Data collection
Recording and analysis of reject data
In the previous section, we described some of the most common reasons for rejects in production. The key to minimizing the reject rate lies in collecting and analyzing relevant data.
This data can come from machine controls, quality inspections, sensors, production planning, or manual entries. Environmental conditions such as temperature and humidity can also provide clues to potential sources of error, same as data about maintenances and downtimes.
This data is needed to:
- identify areas with unusually high reject rates,
- calculate the economic impact of scrap and determine whether countermeasures are worthwhile,
- identify the exact causes of rejects,
- assess the success of optimization measures
- continuously monitor production and respond quickly to rising reject rates.
Requirements to collect reject data
To systematically collect reject data and analyze it for optimization purposes, certain requirements must be met.
Clear (product-specific) definitions of rejects:
- What constitutes defect products?
- Are there different levels of rejects? (e.g. rework possible vs. discarding)
- Categorization based on error sources and/or type of defect
Definition of the inspection process and data sources:
- When and where are rejects identified? (often at multiple points during the production process)
- How is scrap data collected and made available? (e.g. PLCs, inspection systems such as image recognition, manual entries by personnel)
- At which points must data from different inspection processes be consolidated?
Provision of technical infrastructure:
- Can data from all relevant sources be transmitted to the company’s IT systems?
- Which interfaces are required? (Standardization reduces costs and administrative effort)
- At which points can data collection be automated?
A software solution for collecting reject data should offer interfaces both to various data sources and to other IT systems.
Methods for measuring reject data
Reject data can be collected at various points in the production process and in different ways:
Automated collection:
- Machine signals: Modern machines automatically report defective parts (e.g. in case of pressure sensor deviation or dimensional excess)
- Sensors and image processing: Cameras or laser scanners detect surface defects or positional deviations
- Inline measurement systems: Record dimensions, geometries, and surface roughness during the process
Manual data input:
- Inspection by employees at testing stations or during final inspection
- Error reporting via input terminal or mobile app
Semi-automated methods:
- Data entry by operating personnel supported by system suggestions (e.g. dropdown menus for defect causes)
- Barcode or RFID scans for automatic assignment of component, batch, workstation
Practical implementation: Collecting and analyzing reject data with manubes
manubes is a cloud-based platform for production management that enables structured storage, visualization and analysis of production data.
Companies can collect real-time reject data from various sources, including machine controls, software systems such as QM and ERP systems, and manual documentation on mobile devices on the shop floor.
To connect various systems and make data collection as simple as possible, manubes supports standard interfaces such as OPC UA, MQTT, REST and common database interfaces.
Using edge components and outbound communication, this data is securely transmitted from any production site to the cloud.
manubes offers a multitude of features:
- Structuring and storage of data in user-defined data models
- Creation of dashboards to visualize real-time and historical data
- Documentation of maintenance and repairs
- Provision of work instructions and guides for production teams (accessible via mobile devices)
- Quick analysis of saved data with the AI assistant
manubes users have access to no-code design tools to quickly develop suitable applications for various production environments and requirements.
With your personal test account, you can try the different manubes features yourself – completely free and without obligations.
Measures to reduce the reject rate
Automated data transfers
Instead of manually entering data such as machine settings and article numbers, companies can automatically transmit this information from IT systems (e.g. MES) to machine controls.
Replacing manual inputs with automated data transfers not only eliminates human error during data entry but also saves time through automation while reducing the need for extensive training.
Material and tool verification via scan
By using material or tool scans, a verification step can be integrated that requires minimal time and prevents costly mix-ups.
Production staff can scan QR codes or barcodes on packaging materials to confirm that the correct material is being used for the current order. Similarly, tools can be checked for approval and suitability.
Digital work instructions with integrated checks
Digital work instructions can provide step-by-step guidance through a process. Integrated inspections (e.g. checkboxes, photos, input fields for inspection values) ensure that critical steps are properly checked.
Digital work instructions minimize the risk of misinterpretation and support the standardization of procedures, resulting in more consistent product quality.
Automated data transfers
Instead of manually entering data such as machine settings and article numbers, companies can automatically transmit this information from IT systems (e.g. MES) to machine controls.
Replacing manual inputs with automated data transfers not only eliminates human error during data entry but also saves time through automation while reducing the need for extensive training.
Material and tool verification via scan
By using material or tool scans, a verification step can be integrated that requires minimal time and prevents costly mix-ups.
Production staff can scan QR codes or barcodes on packaging materials to confirm that the correct material is being used for the current order. Similarly, tools can be checked for approval and suitability.
Digital work instructions with integrated checks
Digital work instructions can provide step-by-step guidance through a process. Integrated inspections (e.g. checkboxes, photos, input fields for inspection values) ensure that critical steps are properly checked.
Digital work instructions minimize the risk of misinterpretation and support the standardization of procedures, resulting in more consistent product quality.
Automated data transfers
Instead of manually entering data such as machine settings and article numbers, companies can automatically transmit this information from IT systems (e.g. MES) to machine controls.
Replacing manual inputs with automated data transfers not only eliminates human error during data entry but also saves time through automation while reducing the need for extensive training.
Material and tool verification via scan
By using material or tool scans, a verification step can be integrated that requires minimal time and prevents costly mix-ups.
Production staff can scan QR codes or barcodes on packaging materials to confirm that the correct material is being used for the current order. Similarly, tools can be checked for approval and suitability.
Digital work instructions with integrated checks
Digital work instructions can provide step-by-step guidance through a process. Integrated inspections (e.g. checkboxes, photos, input fields for inspection values) ensure that critical steps are properly checked.
Digital work instructions minimize the risk of misinterpretation and support the standardization of procedures, resulting in more consistent product quality.
Documentation of maintenance processes
Comprehensive maintenance documentation helps identify maintenance needs, determine optimal maintenance intervals, and address recurring issues that could lead to scrap at an early stage.
Maintenance activities are recorded systematically and digitally – including timestamps, actions taken, personnel involved, and checklists. This data is available for analysis and can be linked to reject data.
Determing ideal setup/startup parameters
Through systematic trials or data analysis, optimal settings for production start-up can be identified and documented (e.g. temperature profiles, feed rates, ramp-up curves).
These settings can help reduce start-up scrap, thereby addressing one of the most significant categories of rejects in production.
Utilizing a central data platform
Consolidating and structuring production data on a central platform creates a company-wide data foundation and a single point of access for both current and historical data. Companies can assign user permissions and set up interfaces to other systems.
This facilitates communication and collaboration between different teams and reduces the risk of planning errors.
Documentation of maintenance processes
Comprehensive maintenance documentation helps identify maintenance needs, determine optimal maintenance intervals, and address recurring issues that could lead to scrap at an early stage.
Maintenance activities are recorded systematically and digitally – including timestamps, actions taken, personnel involved, and checklists. This data is available for analysis and can be linked to reject data.
Determing ideal setup/startup parameters
Through systematic trials or data analysis, optimal settings for production start-up can be identified and documented (e.g. temperature profiles, feed rates, ramp-up curves).
These settings can help reduce start-up scrap, thereby addressing one of the most significant categories of rejects in production.
Utilizing a central data platform
Consolidating and structuring production data on a central platform creates a company-wide data foundation and a single point of access for both current and historical data. Companies can assign user permissions and set up interfaces to other systems.
This facilitates communication and collaboration between different teams and reduces the risk of planning errors.
Documentation of maintenance processes
Comprehensive maintenance documentation helps identify maintenance needs, determine optimal maintenance intervals, and address recurring issues that could lead to scrap at an early stage.
Maintenance activities are recorded systematically and digitally – including timestamps, actions taken, personnel involved, and checklists. This data is available for analysis and can be linked to reject data.
Determing ideal setup/startup parameters
Through systematic trials or data analysis, optimal settings for production start-up can be identified and documented (e.g. temperature profiles, feed rates, ramp-up curves).
These settings can help reduce start-up scrap, thereby addressing one of the most significant categories of rejects in production.
Utilizing a central data platform
Consolidating and structuring production data on a central platform creates a company-wide data foundation and a single point of access for both current and historical data. Companies can assign user permissions and set up interfaces to other systems.
This facilitates communication and collaboration between different teams and reduces the risk of planning errors.
Conclusion
Reject rate – an important KPI for manufacturers
Minimizing scrap is one of the most common approaches to production optimization. Improvements in various areas can reduce the number of defective products and thus lower the reject rate.
To achieve this, data is collected that provides insights into the causes of product defects and potential countermeasures.
With manubes, we offer a specialized platform for digital production management. Users are able to collect, structure and store various data in a central location. No-code design tools allow for the development of custom solutions for every production environment.
Learn more about manubes!
Cloud-based production management with manubes: Our innovative platform offers specialized tools for connecting production systems, managing and visualizing production data and automating production processes. manubes users benefit from a powerful infrastructure, worldwide access and maximum security.
